Hands-On Genetic Algorithms with Python

Real mutation

One option for applying mutation in real-coded genetic algorithms is to replace any real value with a brand new one, generated randomly. However, this can result in a mutated individual that has no relationship to the original individual.

Another approach is to generate a random real number that resides in the vicinity of the original individual. An example of such a method is the normally distributed (or Gaussian) mutation: a random number is generated using a normal distribution with a mean value of zero and some predetermined standard deviation as shown in the following plot:

Gaussian mutation distribution example

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